You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
MAIA disallows processing of images that are too large to fit into GPU memory. We could implement processing in a sliding window fashion (on smaller image crops, e.g. 4096x4096) if the image is too large. This could allow processing of tiled images or otherwise too large images and make #91 unnecessary.
Ideally, the sliding window should have an overlap so objects are not cut in half and missed. How to merge the detections in the overlap regions? Maybe a greedy method where any two detections with more than 25% (50%?) overlap are merged. In case of many overlapping annotations, those with the largest overlap are preferred.
This could be implemented entirely in Python, using pyvips to cut the images.
The text was updated successfully, but these errors were encountered:
Could novelty detection be performed in a sliding window fashion, too? Otherwise, disable novelty detection if the volume contains too large images (see #91).
MAIA disallows processing of images that are too large to fit into GPU memory. We could implement processing in a sliding window fashion (on smaller image crops, e.g. 4096x4096) if the image is too large. This could allow processing of tiled images or otherwise too large images and make #91 unnecessary.
Ideally, the sliding window should have an overlap so objects are not cut in half and missed. How to merge the detections in the overlap regions? Maybe a greedy method where any two detections with more than 25% (50%?) overlap are merged. In case of many overlapping annotations, those with the largest overlap are preferred.
This could be implemented entirely in Python, using pyvips to cut the images.
The text was updated successfully, but these errors were encountered: